Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Sequential Model Criticism in Probabilistic Expert Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Editorial: Bayesian networks in water resource modelling and management
Environmental Modelling & Software
Bayesian networks in planning a large aquifer in Eastern Mancha, Spain
Environmental Modelling & Software
Environmental Modelling & Software
Public participation modelling using Bayesian networks in management of groundwater contamination
Environmental Modelling & Software
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Using model-based geostatistics to predict lightning-caused wildfires
Environmental Modelling & Software
Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector
Environmental Modelling & Software
Good practice in Bayesian network modelling
Environmental Modelling & Software
A Clustering-Assisted Regression (CAR) approach for developing spatial climate data sets in China
Environmental Modelling & Software
Environmental Modelling & Software
Hi-index | 0.00 |
The impacts of wildfires on ecosystems and the factors contributing to their occurrence are increasingly receiving global attention. Advances in satellite remote sensing and information technology provide an opportunity to study these complex interrelationships. A Bayesian belief network (BBN) model was developed from a set of 12 biotic, abiotic and human variables to determine factors that influence wildfire activity in Swaziland using wildfire data from the Terra and Aqua satellites' Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2001-2007. These were geospatially integrated in the geographic information system (GIS) software ArcView and input into the software Netica for BBN analyses. Land cover, elevation, and climate (mean annual rainfall and mean annual temperature) were found to be strong predictors of wildfire occurrence, while aspect had the least influence on the wildfire occurrence. The model had a high predictive accuracy with an error rate of 9.62%, and an area under the receiver-operating characteristic (ROC) curve of 0.961. The study demonstrates how domain or field knowledge and limited empirical and GIS data can be combined within a BBN model to assist in determining key fire management interventions and lays the foundation for the future development of advanced and dynamic models.